Work

Identification of Comorbidities in Obstructive Sleep Apnea Using Diverse Data and a One-Dimensional Convolutional Neural Network

Year: 2026

Type: article

Abstract: Recent advances in Deep Learning (DL) have enabled the integration of diverse biomedical data for disease prediction and risk stratification. Building on this progress, this study proposes a robust framework for Multi Label Classification (MLC) of comorbidities in patients with Obstructive Sleep Apnea (OSA) using diverse physiological and clinical data sources. A one-dimensional Convolutional Neural Network (1D-CNN) was developed to process and fuse time series signals, oxygen saturation (SpO2), derived SpO2 features, and nasal airflow (FP0), with demographic and physiological parameters, enabling the identification of key comorbidities such as arterial hypertension, diabetes mellitus, and asthma/COPD. The proposed model was trained and fine tuned using the Optuna hyperparameter optimization framework, addressing class imbalance through weighted loss adjustments. Its performance was comprehensively assessed using multi label evaluation metrics, including macro/micro F1-score, AUC-ROC, AUC-PR, subset and partial accuracy, Hamming loss, and Multi Label Confusion Matrix (MLCM) . The 1D-CNN achieved superior predictive performance compared to traditional Machine Learning (ML) classifiers with macro AUC-ROC = 0.7305 and AUC-PR = 0.7497. The model demonstrated consistent behavior across age, gender, and BMI groups, indicating strong generalization and minimal demographic bias. The results confirm that SpO2 and airflow signals inherently encode comorbidity specific physiological patterns, enabling efficient and scalable screening of OSA related comorbidities without the need for full polysomnography. Although the study is limited by data set size, it provides a methodological basis for the application of multi label DL models in clinical decision support systems. Future research should focus on the expansion of multicenter datasets, thereby improving model interpretability and potential clinical adoption.

Source: Sensors

DOI: 10.3390/s26031056